



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]				)       ->							Tuple:

							'''simple docstring'''






							A      						=    384
							A      						=    7
							if "tiny" in model_name:
														A      						=    96
														A      						=    (2, 2, 6, 2)
														A      						=    (3, 6, 12, 24)
							elif "small" in model_name:
														A      						=    96
														A      						=    (2, 2, 18, 2)
														A      						=    (3, 6, 12, 24)
							elif "base" in model_name:
														A      						=    128
														A      						=    (2, 2, 18, 2)
														A      						=    (4, 8, 16, 32)
														A      						=    12
														A      						=    512
							elif "large" in model_name:
														A      						=    192
														A      						=    (2, 2, 18, 2)
														A      						=    (6, 12, 24, 48)
														A      						=    12
														A      						=    768

							# set label information
							A      						=    150
							A      						=    'huggingface/label-files'
							A      						=    'ade20k-id2label.json'
							A      						=    json.load(open(hf_hub_download(lowerCAmelCase__			,	lowerCAmelCase__			,	repo_type='dataset'				)			,	'r'				)				)
							A      						=    {int(lowerCAmelCase__				): v for k, v in idalabel.items()}
							A      						=    {v: k for k, v in idalabel.items()}

							A      						=    SwinConfig(
							    embed_dim=lowerCAmelCase__			,	depths=lowerCAmelCase__			,	num_heads=lowerCAmelCase__			,	window_size=lowerCAmelCase__			,	out_features=['stage1', 'stage2', 'stage3', 'stage4']			,	)
							A      						=    UperNetConfig(
							    backbone_config=lowerCAmelCase__			,	auxiliary_in_channels=lowerCAmelCase__			,	num_labels=lowerCAmelCase__			,	idalabel=lowerCAmelCase__			,	labelaid=lowerCAmelCase__			,	)

							return config






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any				)       ->							Any:

							'''simple docstring'''






							A      						=    []

							# fmt: off
							# stem
							rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')				)
							rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')				)
							rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')				)
							rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')				)
							# stages
							for i in range(len(config.backbone_config.depths				)				):
														for j in range(config.backbone_config.depths[i]				):
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')				)
																					rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')				)

														if i < 3:
																					rename_keys.append((F'''backbone.stages.{i}.downsample.reduction.weight''', F'''backbone.encoder.layers.{i}.downsample.reduction.weight''')				)
																					rename_keys.append((F'''backbone.stages.{i}.downsample.norm.weight''', F'''backbone.encoder.layers.{i}.downsample.norm.weight''')				)
																					rename_keys.append((F'''backbone.stages.{i}.downsample.norm.bias''', F'''backbone.encoder.layers.{i}.downsample.norm.bias''')				)
														rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')				)
														rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')				)

							# decode head
							rename_keys.extend(
							    [
							        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
							        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
							        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
							        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
							    ]				)
							# fmt: on

							return rename_keys






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Optional[int]				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    dct.pop(lowerCAmelCase__				)
							A      						=    val






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Union[str, Any]				)       ->							Tuple:

							'''simple docstring'''






							A      						=    [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
							for i in range(len(backbone_config.depths				)				):
														A      						=    num_features[i]
														for j in range(backbone_config.depths[i]				):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					A      						=    state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''				)
																					A      						=    state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''				)
																					# next, add query, keys and values (in that order) to the state dict
																					A      						=    in_proj_weight[:dim, :]
																					A      						=    in_proj_bias[: dim]
																					A      						=    in_proj_weight[
																					    dim : dim * 2, :
																					]
																					A      						=    in_proj_bias[
																					    dim : dim * 2
																					]
																					A      						=    in_proj_weight[
																					    -dim :, :
																					]
																					A      						=    in_proj_bias[-dim :]
																					# fmt: on






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]				)       ->							Dict:

							'''simple docstring'''






							A						,   A      						=    x.shape
							A      						=    x.reshape(lowerCAmelCase__			,	4			,	in_channel // 4				)
							A      						=    x[:, [0, 2, 1, 3], :].transpose(1			,	2				).reshape(lowerCAmelCase__			,	lowerCAmelCase__				)
							return x






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]				)       ->							List[str]:

							'''simple docstring'''






							A						,   A      						=    x.shape
							A      						=    x.reshape(lowerCAmelCase__			,	in_channel // 4			,	4				)
							A      						=    x[:, :, [0, 2, 1, 3]].transpose(1			,	2				).reshape(lowerCAmelCase__			,	lowerCAmelCase__				)

							return x






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    x.shape[0]
							A      						=    x.reshape(4			,	in_channel // 4				)
							A      						=    x[[0, 2, 1, 3], :].transpose(0			,	1				).reshape(lowerCAmelCase__				)
							return x






def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    x.shape[0]
							A      						=    x.reshape(in_channel // 4			,	4				)
							A      						=    x[:, [0, 2, 1, 3]].transpose(0			,	1				).reshape(lowerCAmelCase__				)
							return x






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Union[str, Any]				)       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    {
							    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
							    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
							    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
							    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
							}
							A      						=    model_name_to_url[model_name]
							A      						=    torch.hub.load_state_dict_from_url(lowerCAmelCase__			,	map_location='cpu'			,	file_name=lowerCAmelCase__				)[
							    'state_dict'
							]

							for name, param in state_dict.items():
														print(lowerCAmelCase__			,	param.shape				)

							A      						=    get_upernet_config(lowerCAmelCase__				)
							A      						=    UperNetForSemanticSegmentation(lowerCAmelCase__				)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														A      						=    state_dict.pop(lowerCAmelCase__				)
														if "bn" in key:
																					A      						=    key.replace('bn'			,	'batch_norm'				)
														A      						=    val

							# rename keys
							A      						=    create_rename_keys(lowerCAmelCase__				)
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)
							read_in_q_k_v(lowerCAmelCase__			,	config.backbone_config				)

							# fix downsample parameters
							for key, value in state_dict.items():
														if "downsample" in key:
																					if "reduction" in key:
																												A      						=    reverse_correct_unfold_reduction_order(lowerCAmelCase__				)
																					if "norm" in key:
																												A      						=    reverse_correct_unfold_norm_order(lowerCAmelCase__				)

							model.load_state_dict(lowerCAmelCase__				)

							# verify on image
							A      						=    'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
							A      						=    Image.open(requests.get(lowerCAmelCase__			,	stream=lowerCAmelCase__				).raw				).convert('RGB'				)

							A      						=    SegformerImageProcessor()
							A      						=    processor(lowerCAmelCase__			,	return_tensors='pt'				).pixel_values

							with torch.no_grad():
														A      						=    model(lowerCAmelCase__				)
														A      						=    outputs.logits

							print(logits.shape				)
							print('First values of logits:'			,	logits[0, 0, :3, :3]				)
							# assert values
							if model_name == "upernet-swin-tiny":
														A      						=    torch.tensor(
														    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]				)
							elif model_name == "upernet-swin-small":
														A      						=    torch.tensor(
														    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]				)
							elif model_name == "upernet-swin-base":
														A      						=    torch.tensor(
														    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]				)
							elif model_name == "upernet-swin-large":
														A      						=    torch.tensor(
														    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]				)
							print('Logits:'			,	outputs.logits[0, 0, :3, :3]				)
							assert torch.allclose(outputs.logits[0, 0, :3, :3]			,	lowerCAmelCase__			,	atol=1E-4				)
							print('Looks ok!'				)

							if pytorch_dump_folder_path is not None:
														print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
														model.save_pretrained(lowerCAmelCase__				)
														print(F'''Saving processor to {pytorch_dump_folder_path}'''				)
														processor.save_pretrained(lowerCAmelCase__				)

							if push_to_hub:
														print(F'''Pushing model and processor for {model_name} to hub'''				)
														model.push_to_hub(F'''openmmlab/{model_name}'''				)
														processor.push_to_hub(F'''openmmlab/{model_name}'''				)


if __name__ == "__main__":
				__snake_case     :str			       =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='upernet-swin-tiny',
				    type=str,
				    choices=[F'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
				    help='Name of the Swin + UperNet model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				__snake_case     :int			       =parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""



from collections import Counter
from timeit import timeit
def      UpperCAmelCase     (       _lowercase :   str = ""			,   )						-> bool:


	"""simple docstring"""




	return sum(c % 2 for c in Counter(input_str.replace(''' '''			,   ''''''	).lower()	).values()	) < 2
def      UpperCAmelCase     (       _lowercase :   str = ""	)						-> bool:


	"""simple docstring"""




	if len(_lowercase	) == 0:
		return True
	lowerCAmelCase_      							=			input_str.replace(''' '''			,   ''''''	).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	lowerCAmelCase_      							=			{}

	for character in lower_case_input_str:
		lowerCAmelCase_      							=			character_freq_dict.get(_lowercase			,   0	) + 1
	lowerCAmelCase_      							=			0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True
def      UpperCAmelCase     (       _lowercase :   str = ""	)						-> None:


	"""simple docstring"""




	print('''\nFor string = '''			,   _lowercase			,   ''':'''	)
	print(
	    '''> can_string_be_rearranged_as_palindrome_counter()'''			,   '''\tans ='''			,   can_string_be_rearranged_as_palindrome_counter(_lowercase	)			,   '''\ttime ='''			,   timeit(
	        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''			,   setup='''import __main__ as z'''			,   )			,   '''seconds'''			,   )
	print(
	    '''> can_string_be_rearranged_as_palindrome()'''			,   '''\tans ='''			,   can_string_be_rearranged_as_palindrome(_lowercase	)			,   '''\ttime ='''			,   timeit(
	        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''			,   setup='''import __main__ as z'''			,   )			,   '''seconds'''			,   )


if __name__ == "__main__":
		lowercase_							=							input(
		    'Enter string to determine if it can be rearranged as a palindrome or not: '
		).strip()
		benchmark(check_str)
		lowercase_							=							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")